Evidence from FDA Compliance Check Inspections on the Effectiveness of Retail Access Controls on Tobacco Purchases

Objectives: Those underage should not use tobacco products, including electronic nicotine delivery systems. A technologically-based solution developed by Juul Labs Inc to restrict underage access seeks to automate transactions, structurally mandate age-verification, and limit the quantity of JUUL products purchased per transaction. A pilot of this standards-based approach, referred to as RACS™ (Retail Access Control Standards), was conducted to assess efficacy. Methods: RACS was implemented at 171 stores within 3 retail chains selling tobacco products in 3 states. "Secret shopper" compliance audits, in which a mystery shopper attempted to purchase JUUL products, were conducted at participating stores before and after implementation of RACS, to test compliance with age-verification and product-quantity limits. Audit failure rates were compared pre- and post-RACS implementation to assess effectiveness. Comparisons were made overall, by chain, state, and failure type. Results: A total of 3990 audits were conducted. We found large, statistically significant reductions (p < .001) in failure rates for age-verification and compliance with product-quantity limits after implementing RACS, as compared to pre-RACS rates, declining to near zero. Conclusions: Utilizing enhanced access controls can be part of a comprehensive, evidence-based tobacco-control strategy to address underage use of all tobacco products, including JUUL.

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Confidentiality of Statistical Records: A Threat-Monitoring Scheme for On Line Dialogue

Methods of Information in Medicine ◽

10.1055/s-0038-1635718 ◽

1976 ◽

Vol 15 (01) ◽

pp. 36-42 ◽

Cited By ~ 14

Author(s):

J. Schlörer

Keyword(s):

Statistical Data ◽

Cost Benefit ◽

Data Bank ◽

High Ratio ◽

Point Of View ◽

Data Sets ◽

Monitoring Scheme ◽

Access Controls ◽

On Line ◽

Bona Fide

From a statistical data bank containing only anonymous records, the records sometimes may be identified and then retrieved, as personal records, by on line dialogue. The risk mainly applies to statistical data sets representing populations, or samples with a high ratio n/N. On the other hand, access controls are unsatisfactory as a general means of protection for statistical data banks, which should be open to large user communities. A threat monitoring scheme is proposed, which will largely block the techniques for retrieval of complete records. If combined with additional measures (e.g., slight modifications of output), it may be expected to render, from a cost-benefit point of view, intrusion attempts by dialogue valueless, if not absolutely impossible. The bona fide user has to pay by some loss of information, but considerable flexibility in evaluation is retained. The proposal of controlled classification included in the scheme may also be useful for off line dialogue systems.

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Electronic fee collection. Compliance check communication for autonomous systems

10.3403/30183051 ◽

2009 ◽

Keyword(s):

Autonomous Systems ◽

Compliance Check

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Categorizing Touch-Input Locations from Touchscreen Device Interfaces via On-Board Mechano-Acoustic Transducers

Applied Sciences ◽

10.3390/app11114834 ◽

2021 ◽

Vol 11 (11) ◽

pp. 4834

Author(s):

Kai Ren Teo ◽

Balamurali B T ◽

Jianying Zhou ◽

Jer-Ming Chen

Keyword(s):

User Interaction ◽

Data Access ◽

Machine Learning Techniques ◽

Acoustic Features ◽

Touch Input ◽

Acoustic Transducers ◽

Learning Techniques ◽

Access Controls ◽

Input Location ◽

Mitigation Methods

Many mobile electronics devices, including smartphones and tablets, require the user to interact physically with the device via tapping the touchscreen. Conveniently, these compact devices are also equipped with high-precision transducers such as accelerometers and microphones, integrated mechanically and designed on-board to support a range of user functionalities. However, unintended access to these transducer signals (bypassing normal on-board data access controls) may allow sensitive user interaction information to be detected and thereby exploited. In this study, we show that acoustic features extracted from the on-board microphone signals, supported with accelerometer and gyroscope signals, may be used together with machine learning techniques to successfully determine the user’s touch input location on a touchscreen: our ensemble model, namely the random forest model, predicts touch input location with up to 86% accuracy in a realistic scenario. Accordingly, we present the approach and techniques used, the performance of the model developed, and also discuss limitations and possible mitigation methods to thwart possible exploitation of such unintended signal channels.

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